We built an AI follow-up assistant for surgical centres that talks with patients after discharge and flags pain or confusion to nurses. Patient adherence rose 30%, and readmissions fell 12%.
Industry: HealthcareService: AI & AutomationUpdated
Overview
The challenge
After discharge, the client relied on automated SMS reminders and manual nurse calls. Patients dealing with confusion or anxiety after surgery ignored these fixed prompts, so 40% dropped out of follow-up and some were readmitted when it could have been prevented.
Preventable readmissions hurt the centres' CMS reimbursement scores. Nurses spent hours calling stable patients, and patients saw the generic texts as spam.
High readmission rates for preventable complications after surgery directly affected CMS reimbursement scores.
80% of the manual follow-up queue was noise, which wasted thousands of specialist clinical hours on stable patients.
Patients saw generic SMS reminders as spam, which lowered their trust and how closely they followed their care plans.
The solution
Live discharge events from the health record (ADT messages) start the AI workflows, so every follow-up happens within the 48-hour recovery window that matters most.
AI assistants hold multi-turn conversations with patients. The engine reads signs of pain, confusion and urgency, and ranks patients automatically.
Every conversation is summarised in the EHR with a 100% audit trail, so a nurse taking an escalated call can see the full context.
Before and after
Before
Generic reminders
One-way texts sent to every patient, whatever their recovery or type of surgery.
After
Reads how patients feel
Conversational AI picks up signs of distress and raises the urgency while the conversation is happening.
Before
Separate data sources
Follow-up systems were cut off from live EHR discharge and readmission data.
After
Linked to the health record
Two-way sync keeps every follow-up up to date with the live clinical record.
Before
Limited by headcount
How many patients could be prioritised depended on nurse numbers and the time each call took.
After
Automated follow-up
The system handles 100% of follow-ups and escalates only the top 10% of high-risk cases to nurses.
Key features
The AI identified patients reporting uncontrolled pain or confusion about their medication with 94% accuracy against the clinical gold standard.
Business impact
Precise escalation
Models run in a zero-persistence environment, so patient health information never leaves the clinical cloud.
Business impact
100% of patient data stays in the clinical cloud
Automatic outreach goes out at 6, 24 and 72 hours after surgery, based on live surgical data.
Business impact
30% adherence lift
Results
30% Adherence Lift
Adherence rose from 60% to 90%, a 30-point lift, as the AI brought back patients who would usually have dropped out of care.
Before: 60%After: 90%
80% Less Queue Load
Routine questions from stable patients are handled automatically, so nurses spend their time on the patients who need them.
Before: OverloadedAfter: 80% Automated
12% Readmission Rate
Spotting distress early let nurses step in before patients needed an emergency visit, cutting 30-day readmissions by 12%.
Before: -5%After: -12%
Faster delivery
These modules saved about 10 weeks of setup, which went into the EHR integration and into tuning distress detection with the clinical team.
Gave us a tested base for reading pain, confusion and urgency in patient replies.
Kept patient data encrypted and inside the clinical cloud while the AI handled each conversation.
Runs the 6, 24 and 72-hour check-in schedule from live discharge events.
Passes patients showing early warning signs to a nurse before their condition can lead to an emergency visit.
Trust and control
Every patient conversation is encrypted and processed inside the client's cloud, covered by its business associate agreement (BAA).
HIPAA safeguardsEach escalation includes a structured reason code, such as rising pain in the patient's replies, so nurses know why it was raised.
Verifiable dataCloud language AI services handle discharge spikes across several specialties at the same time.
Ready to growThe client owns 100% of the sentiment models and care coordination workflows.
100% ownedThe AI holds conversations with patients after discharge and reads their replies for signs of pain, confusion and urgency. It identified patients with uncontrolled pain or medication confusion with 94% accuracy. Stable patients are handled automatically, and only the top 10% of high-risk cases are escalated, each with a reason code.
Follow-up adherence rose from 60% to 90%, a 30-point lift. Readmissions within 30 days fell by 12%, because nurses could step in before patients needed an emergency visit. The load on the nurse call queue dropped by 80%, as routine questions from stable patients are now handled automatically.
Every conversation is encrypted and processed inside the client's own cloud, covered by its business associate agreement, and models run in a zero-persistence environment. Patient health information never leaves that clinical cloud. Each conversation is summarised in the EHR with a full audit trail, and the platform was built to HIPAA requirements.
If patients drop out of follow-up while nurses are stuck calling stable cases, we can help you build check-ins that send only urgent cases to your team.